Fig. 1
Schematic diagram of the neural network architecture for the a convolutional neural network (CNN) and the b multilayer perceptron (MLP). Both the proposed CNN and MLP consist of 4 feature extraction layers, for which they learnt a mapping between the input spectra and multiple pHe compartments. To compare the performance of CNN and MLP in correctly predicting pHe compartments, the architectures were set to a similar number of weights, ≈ 8000. The length of the spectrum or feature maps, which are used as the input to each next convolutional or dense layer, are shown in the square brackets. In CNN, the lengths are scaled logarithmically. CNN: The number of filters is 4, 4, 8, and 8. The sizes of the convolutional kernel are shown in the round brackets. MLP: The number of neurons is 16, 16, 32, and 32. Dense layers are represented with half (MLP) or quarter (CNN) the number of nodes, except for output layers